Information needed to make a critical decision regarding the design or field management of one or more manufactured components, such as a gas turbine engine component, can be developed during any phase of the component's lifecycle. A decision can be improved by reducing uncertainty in the data used as the basis for the decision. In the gas turbine engine industry, there are many different potential sources of uncertainty, and the sources of uncertainty may change over the course of a component's lifecycle.
Due to the complex nature of gas turbine engine systems and the associated mathematical predictions, existing decision tools do not provide a closed-form mathematical solution for the gas turbine engine industry. Existing empirical models and system architectures attempt to approximate system behavior based on extensive testing and analysis. However, for representative testing and analysis to be applied, existing systems need to assume certain environmental and operational boundary conditions. These assumptions constrain the relevant design domain that may satisfy a particular belief about the empirical parameter estimates or system interactions. Extrapolation of models with empirical parameter estimates or assumed system influence bounded by the boundary condition assumptions has often resulted in inaccurate determinations of real-world component behavior in service. The resultant implications include increased costs due to overly conservative design practices and inaccurate predictions leading to component failure in operation.
A probabilistic model, such as a Bayesian network, can be used to depict relationships between random variables and their conditional dependencies as a graph in which the random variables are represented as nodes, the conditional dependencies are designated by the edges between the nodes, and the lack of an edge between two nodes indicates that the random variables represented by the unconnected nodes are conditionally independent of one another. A probability distribution is associated with each of the nodes in the model.